{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pylift example on the Lalonde dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lalonde dataset was used to evaluate propensity score in the paper:\n",
    "\n",
    "Dehejia, R., & Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs. Journal of the American Statistical Association, 94(448), 1053-1062. doi:10.2307/2669919\n",
    "\n",
    "\n",
    "Lalonde dataset is now included in \"Matching\" package available in R. \n",
    "\n",
    "http://sekhon.berkeley.edu/matching/lalonde.html\n",
    "\n",
    "\n",
    "The original Lalonde dataset was used in the paper:\n",
    "\n",
    "LaLonde, R. (1986). Evaluating the Econometric Evaluations of Training Programs with Experimental Data. The American Economic Review, 76(4), 604-620. Retrieved from http://www.jstor.org/stable/1806062\n",
    "\n",
    "\n",
    "To use for pylift in Python, Lalonde dataset can be retrieved from:\n",
    "\n",
    "https://users.nber.org/~rdehejia/nswdata.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-02T22:52:56.325381Z",
     "start_time": "2018-10-02T22:52:56.000938Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-02T22:52:57.044432Z",
     "start_time": "2018-10-02T22:52:56.457251Z"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np, matplotlib as mpl, matplotlib.pyplot as plt, pandas as pd\n",
    "from pylift import TransformedOutcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load datasets, concatenate, and create features (align with \"Matching\" package available in R)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = ['treat', 'age', 'educ', 'black', 'hisp', 'married', 'nodegr','re74','re75','re78']\n",
    "control_df = pd.read_csv('http://www.nber.org/~rdehejia/data/nswre74_control.txt', sep='\\s+', header = None, names = cols)\n",
    "treated_df = pd.read_csv('http://www.nber.org/~rdehejia/data/nswre74_treated.txt', sep='\\s+', header = None, names = cols)\n",
    "lalonde_df = pd.concat([control_df, treated_df], ignore_index=True)\n",
    "lalonde_df['u74'] = np.where(lalonde_df['re74'] == 0, 1.0, 0.0)\n",
    "lalonde_df['u75'] = np.where(lalonde_df['re75'] == 0, 1.0, 0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the input Data Frame for pylift."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = lalonde_df[['nodegr', 'black', 'hisp', 'age', 'educ', 'married', 'u74', 'u75', 'treat', 're78']].copy()\n",
    "df.rename(columns={'treat':'Treatment', 're78':'Outcome'}, inplace=True)\n",
    "df['Outcome'] = np.where(df['Outcome'] > 0, 1.0, 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>nodegr</th>\n",
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       "      <th>Outcome</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>445 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nodegr  black  hisp   age  educ  married  u74  u75  Treatment  Outcome\n",
       "0       1.0    1.0   0.0  23.0  10.0      0.0  1.0  1.0        0.0      0.0\n",
       "1       0.0    0.0   0.0  26.0  12.0      0.0  1.0  1.0        0.0      1.0\n",
       "2       1.0    1.0   0.0  22.0   9.0      0.0  1.0  1.0        0.0      0.0\n",
       "3       1.0    1.0   0.0  18.0   9.0      0.0  1.0  1.0        0.0      1.0\n",
       "4       1.0    1.0   0.0  45.0  11.0      0.0  1.0  1.0        0.0      1.0\n",
       "5       1.0    1.0   0.0  18.0   9.0      0.0  1.0  1.0        0.0      1.0\n",
       "..      ...    ...   ...   ...   ...      ...  ...  ...        ...      ...\n",
       "439     1.0    0.0   1.0  29.0  10.0      0.0  1.0  0.0        1.0      1.0\n",
       "440     0.0    1.0   0.0  33.0  12.0      1.0  0.0  0.0        1.0      1.0\n",
       "441     0.0    1.0   0.0  25.0  14.0      1.0  0.0  0.0        1.0      1.0\n",
       "442     1.0    1.0   0.0  35.0   9.0      1.0  0.0  0.0        1.0      1.0\n",
       "443     1.0    1.0   0.0  35.0   8.0      1.0  0.0  0.0        1.0      1.0\n",
       "444     1.0    1.0   0.0  33.0  11.0      1.0  0.0  0.0        1.0      1.0\n",
       "\n",
       "[445 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-tabulation of Treatment and Outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Treatment</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0.0</th>\n",
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       "      <td>137</td>\n",
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       "      <th>1.0</th>\n",
       "      <td>168</td>\n",
       "      <td>140</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>260</td>\n",
       "      <td>185</td>\n",
       "      <td>445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Treatment  0.0  1.0  All\n",
       "Outcome                 \n",
       "0.0         92   45  137\n",
       "1.0        168  140  308\n",
       "All        260  185  445"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Outcome'], df['Treatment'], margins = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-tabulation of Treatment and Outcome in Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Treatment</th>\n",
       "      <th>0.0</th>\n",
       "      <th>1.0</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.206742</td>\n",
       "      <td>0.101124</td>\n",
       "      <td>0.307865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.377528</td>\n",
       "      <td>0.314607</td>\n",
       "      <td>0.692135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.584270</td>\n",
       "      <td>0.415730</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Treatment       0.0       1.0       All\n",
       "Outcome                                \n",
       "0.0        0.206742  0.101124  0.307865\n",
       "1.0        0.377528  0.314607  0.692135\n",
       "All        0.584270  0.415730  1.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['Outcome'], df['Treatment'], margins = True, normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>nodegr</th>\n",
       "      <th>black</th>\n",
       "      <th>hisp</th>\n",
       "      <th>age</th>\n",
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       "      <th>married</th>\n",
       "      <th>u74</th>\n",
       "      <th>u75</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Outcome</th>\n",
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       "    <tr>\n",
       "      <th>nodegr</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.041948</td>\n",
       "      <td>0.086638</td>\n",
       "      <td>-0.112838</td>\n",
       "      <td>-0.635575</td>\n",
       "      <td>-0.038554</td>\n",
       "      <td>0.049934</td>\n",
       "      <td>0.079798</td>\n",
       "      <td>-0.151012</td>\n",
       "      <td>-0.045548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>0.041948</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.693969</td>\n",
       "      <td>0.087171</td>\n",
       "      <td>0.045405</td>\n",
       "      <td>0.023731</td>\n",
       "      <td>0.030152</td>\n",
       "      <td>0.038685</td>\n",
       "      <td>0.021602</td>\n",
       "      <td>-0.167117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hisp</th>\n",
       "      <td>0.086638</td>\n",
       "      <td>-0.693969</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.090157</td>\n",
       "      <td>-0.144835</td>\n",
       "      <td>0.009064</td>\n",
       "      <td>-0.046158</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>-0.084066</td>\n",
       "      <td>0.120627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.112838</td>\n",
       "      <td>0.087171</td>\n",
       "      <td>-0.090157</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.022964</td>\n",
       "      <td>0.210072</td>\n",
       "      <td>0.102457</td>\n",
       "      <td>0.075597</td>\n",
       "      <td>0.052977</td>\n",
       "      <td>-0.010435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educ</th>\n",
       "      <td>-0.635575</td>\n",
       "      <td>0.045405</td>\n",
       "      <td>-0.144835</td>\n",
       "      <td>0.022964</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081613</td>\n",
       "      <td>-0.092843</td>\n",
       "      <td>-0.127603</td>\n",
       "      <td>0.070890</td>\n",
       "      <td>0.004852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>-0.038554</td>\n",
       "      <td>0.023731</td>\n",
       "      <td>0.009064</td>\n",
       "      <td>0.210072</td>\n",
       "      <td>0.081613</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080614</td>\n",
       "      <td>-0.096973</td>\n",
       "      <td>0.046531</td>\n",
       "      <td>0.027178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u74</th>\n",
       "      <td>0.049934</td>\n",
       "      <td>0.030152</td>\n",
       "      <td>-0.046158</td>\n",
       "      <td>0.102457</td>\n",
       "      <td>-0.092843</td>\n",
       "      <td>-0.080614</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.726574</td>\n",
       "      <td>-0.046647</td>\n",
       "      <td>-0.061988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u75</th>\n",
       "      <td>0.079798</td>\n",
       "      <td>0.038685</td>\n",
       "      <td>0.011191</td>\n",
       "      <td>0.075597</td>\n",
       "      <td>-0.127603</td>\n",
       "      <td>-0.096973</td>\n",
       "      <td>0.726574</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087400</td>\n",
       "      <td>-0.061491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <td>-0.151012</td>\n",
       "      <td>0.021602</td>\n",
       "      <td>-0.084066</td>\n",
       "      <td>0.052977</td>\n",
       "      <td>0.070890</td>\n",
       "      <td>0.046531</td>\n",
       "      <td>-0.046647</td>\n",
       "      <td>-0.087400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.118087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Outcome</th>\n",
       "      <td>-0.045548</td>\n",
       "      <td>-0.167117</td>\n",
       "      <td>0.120627</td>\n",
       "      <td>-0.010435</td>\n",
       "      <td>0.004852</td>\n",
       "      <td>0.027178</td>\n",
       "      <td>-0.061988</td>\n",
       "      <td>-0.061491</td>\n",
       "      <td>0.118087</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             nodegr     black      hisp       age      educ   married  \\\n",
       "nodegr     1.000000  0.041948  0.086638 -0.112838 -0.635575 -0.038554   \n",
       "black      0.041948  1.000000 -0.693969  0.087171  0.045405  0.023731   \n",
       "hisp       0.086638 -0.693969  1.000000 -0.090157 -0.144835  0.009064   \n",
       "age       -0.112838  0.087171 -0.090157  1.000000  0.022964  0.210072   \n",
       "educ      -0.635575  0.045405 -0.144835  0.022964  1.000000  0.081613   \n",
       "married   -0.038554  0.023731  0.009064  0.210072  0.081613  1.000000   \n",
       "u74        0.049934  0.030152 -0.046158  0.102457 -0.092843 -0.080614   \n",
       "u75        0.079798  0.038685  0.011191  0.075597 -0.127603 -0.096973   \n",
       "Treatment -0.151012  0.021602 -0.084066  0.052977  0.070890  0.046531   \n",
       "Outcome   -0.045548 -0.167117  0.120627 -0.010435  0.004852  0.027178   \n",
       "\n",
       "                u74       u75  Treatment   Outcome  \n",
       "nodegr     0.049934  0.079798  -0.151012 -0.045548  \n",
       "black      0.030152  0.038685   0.021602 -0.167117  \n",
       "hisp      -0.046158  0.011191  -0.084066  0.120627  \n",
       "age        0.102457  0.075597   0.052977 -0.010435  \n",
       "educ      -0.092843 -0.127603   0.070890  0.004852  \n",
       "married   -0.080614 -0.096973   0.046531  0.027178  \n",
       "u74        1.000000  0.726574  -0.046647 -0.061988  \n",
       "u75        0.726574  1.000000  -0.087400 -0.061491  \n",
       "Treatment -0.046647 -0.087400   1.000000  0.118087  \n",
       "Outcome   -0.061988 -0.061491   0.118087  1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply pylift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=10)]: Done  12 tasks      | elapsed:   16.0s\n",
      "[Parallel(n_jobs=10)]: Done  60 out of  60 | elapsed:   17.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed 0 finished.\n",
      "Seed 1 finished.\n",
      "Seed 2 finished.\n",
      "Seed 3 finished.\n",
      "Seed 4 finished.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27b300fdef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oFxUoRWSVgqWIyJhIXcpSe4k4jZnKT1HMFfvdJLlJBb9AjRrtuE2QV7CU3aNA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\nno997LqG6EqohwdQIpj2mT3DzWkdYXoAsCR+T9NVOJyuarXCo48+wNq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15ll1MIy4JuplWt7KDzajuIqik2c1QGg7YM2qc2ZTQeZSAYoOzNb7gjIlx++a8ZG6sxPDz3xL55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GYThNFE0jhMnAwAmEkPT2HiHCAWnhSrh6ucVdd+3i8ccP3SAqAVSrhdYas7q9dhgLNp6yYzDj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b2faac710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "up = TransformedOutcome(df, col_treatment='Treatment', col_outcome='Outcome', stratify=df['Treatment'])\n",
    "up.randomized_search(n_iter=20, n_jobs=10, random_state=1)\n",
    "up.shuffle_fit(params=up.rand_search_.best_params_, nthread=30, iterations=5)\n",
    "up.plot(show_shuffle_fits=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-04-25T19:45:02.791303Z",
     "start_time": "2018-04-25T19:45:02.760722Z"
    }
   },
   "source": [
    "# *<font color=\"#668b8b\">Check docstrings (and let us know if anything is weird)!</font>*"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
